run_fasttext.py

from gensim.models.fasttext import FastText
import nltk
wpt = nltk.WordPunctTokenizer()
tokenized_corpus = [wpt.tokenize(document) for document in norm_bible]

# Set values for various parameters
feature_size = 100    # Word vector dimensionality  
window_context = 50          # Context window size                                                                                    
min_word_count = 5   # Minimum word count                        
sample = 1e-3   # Downsample setting for frequent words

# sg decides whether to use the skip-gram model (1) or CBOW (0)
ft_model = FastText(tokenized_corpus, size=feature_size, window=window_context, 
                    min_count=min_word_count,sample=sample, sg=1, iter=50)
                    
                    
# view similar words based on gensim's FastText model
similar_words = {search_term: [item[0] for item in ft_model.wv.most_similar([search_term], topn=5)]
                  for search_term in ['god', 'jesus', 'noah', 'egypt', 'john', 'gospel', 'moses','famine']}
print(similar_words)             

from sklearn.decomposition import PCA

words = sum([[k] + v for k, v in similar_words.items()], [])
wvs = ft_model.wv[words]

pca = PCA(n_components=2)
np.set_printoptions(suppress=True)
P = pca.fit_transform(wvs)
labels = words

plt.figure(figsize=(18, 10))
plt.scatter(P[:, 0], P[:, 1], c='lightgreen', edgecolors='g')
for label, x, y in zip(labels, P[:, 0], P[:, 1]):
    plt.annotate(label, xy=(x+0.06, y+0.03), xytext=(0, 0), textcoords='offset points')